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Record W3211264963 · doi:10.12927/hcq.2021.26619

Leadership during a Crisis: Observations by Emerging Leaders during the COVID-19 Pandemic

2021· article· en· W3211264963 on OpenAlexaffvenue
Nelson Wendy, Arlinda Ruco, Isser Dubinsky

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity Health NetworkSt. Michael's HospitalToronto Public Health
Fundersnot available
KeywordsPandemicPublic relationsCoronavirus disease 2019 (COVID-19)Health carePsychologyCrisis communicationCrisis managementPolitical scienceCrisis responseMedicine

Abstract

fetched live from OpenAlex

During crises, leaders must address fear, give people a role and purpose and emphasize experimentation, learning and self-care. A survey of emerging health leaders rated the frequency with which they observed their organizational leaders and themselves engage in these crisis leadership functions during the COVID-19 pandemic. Findings revealed significant differences between emerging and more experienced leaders' behaviours in acknowledging fears and providing reassurance, managing individual health and role modelling good self-care and encouraging others to practise good self-care. Emerging leaders rated themselves as engaging in these behaviours more frequently. Barriers preventing these practices included heavy workloads and communication issues. Enablers included good team dynamics, clear communication and incorporating technology. Implications for leadership are presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.281
GPT teacher head0.440
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes2
Has abstractyes

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